We evaluate our network on ModelNet40 [1]} for 3D object classification task. ModelNet40 contains 12311 meshed CAD models from 40 categories. The dataset has 9843 objects for training and 2468 objects for testing. We prepare the dataset following PointNet [2]} conventions. We random sample \(N\)  points and only use normal as input feature. As illustrated in Figure REF , the output from all PVC layers are concatenated to form a global feature. Max pooling followed by a fully connected layer are then used to produce a classification score.
